Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
117
result(s) for
"Watson, Bruce W"
Sort by:
Prevalence of symptoms, comorbidities, fibrin amyloid microclots and platelet pathology in individuals with Long COVID/Post-Acute Sequelae of COVID-19 (PASC)
Background
Fibrin(ogen) amyloid microclots and platelet hyperactivation previously reported as a novel finding in South African patients with the coronavirus 2019 disease (COVID-19) and Long COVID/Post-Acute Sequelae of COVID-19 (PASC), might form a suitable set of foci for the clinical treatment of the symptoms of Long COVID/PASC. A Long COVID/PASC Registry was subsequently established as an online platform where patients can report Long COVID/PASC symptoms and previous comorbidities.
Methods
In this study, we report on the comorbidities and persistent symptoms, using data obtained from 845 South African Long COVID/PASC patients. By using a previously published scoring system for fibrin amyloid microclots and platelet pathology, we also analysed blood samples from 80 patients, and report the presence of significant fibrin amyloid microclots and platelet pathology in all cases.
Results
Hypertension, high cholesterol levels (dyslipidaemia), cardiovascular disease and type 2 diabetes mellitus (T2DM) were found to be the most important comorbidities. The gender balance (70% female) and the most commonly reported Long COVID/PASC symptoms (fatigue, brain fog, loss of concentration and forgetfulness, shortness of breath, as well as joint and muscle pains) were comparable to those reported elsewhere. These findings confirmed that our sample was not atypical. Microclot and platelet pathologies were associated with Long COVID/PASC symptoms that persisted after the recovery from acute COVID-19.
Conclusions
Fibrin amyloid microclots that block capillaries and inhibit the transport of O
2
to tissues, accompanied by platelet hyperactivation, provide a ready explanation for the symptoms of Long COVID/PASC. Removal and reversal of these underlying endotheliopathies provide an important treatment option that urgently warrants controlled clinical studies to determine efficacy in patients with a diversity of comorbidities impacting on SARS-CoV-2 infection and COVID-19 severity. We suggest that our platelet and clotting grading system provides a simple and cost-effective diagnostic method for early detection of Long COVID/PASC as a major determinant of effective treatment, including those focusing on reducing clot burden and platelet hyperactivation.
Journal Article
Data sharing and data governance in sub-Saharan Africa: Perspectives from researchers and scientists engaged in data-intensive research
The data ecosystem is complex and involves multiple stakeholders. Researchers and scientists engaging in data-intensive research collect, analyse, store, manage and share large volumes of data. Consequently, capturing researchers' and scientists' views from multidisciplinary fields on data use, sharing and governance adds an important African perspective to emerging debates. We conducted a descriptive cross-sectional survey and received 160 responses from researchers and scientists representing 43 sub-Saharan African countries. Whilst most respondents were satisfied with institutional data storage processes, 40% indicated that their organisations or institutions did not have a formally established process for storing data beyond the life cycle of the project. Willingness to share data was generally high, but increased when data privacy was ensured. Robust governance frameworks increased the willingness to share, as did the regulation of access to data on shared platforms. Incentivising data sharing remains controversial. Respondents were satisfied with exchanging their data for co-authorship on publications (89.4%) and collaboration on projects (77.6%). However, respondents were split almost equally in terms of sharing their data for commercial gain. Regarding the process of managing data, 40.6% indicated that their organisations do not provide training on best practices for data management. This could be related to a lack of resources, chronic institutional under-investment, and suboptimal research training and mentorship in sub-Saharan Africa. The sustainability of data sharing may require ethical incentive structures to further encourage researchers and scientists. Tangible infrastructure to facilitate such sharing is a prerequisite. Capacity development in data governance for researchers and scientists is sorely needed.
Data sharing is necessary to advance science, yet there are many constraints. In this study, we explored factors that promote a willingness to share, as well as constraining factors. Seeking potential solutions to improve data sharing is a scientific and ethical imperative. The standardisation of basic data sharing and data transfer agreements, and the development of a Data Access Committee will strengthen data governance and facilitate responsible data sharing in sub-Saharan Africa. Funders, institutions, researchers and scientists ought to jointly contribute to fair and equitable data use and sharing during and beyond the life cycle of research projects.
Journal Article
Data sharing: A Long COVID perspective, challenges, and road map for the future
‘Long COVID’ is the term used to describe the phenomenon in which patients who have survived a COVID-19 infection continue to experience prolonged SARS-CoV-2 symptoms. Millions of people across the globe are affected by Long COVID. Solving the Long COVID conundrum will require drawing upon the lessons of the COVID-19 pandemic, during which thousands of experts across diverse disciplines such as epidemiology, genomics, medicine, data science, and computer science collaborated, sharing data and pooling resources to attack the problem from multiple angles. Thus far, there has been no global consensus on the definition, diagnosis, and most effective treatment of Long COVID. In this work, we examine the possible applications of data sharing and data science in general with a view to, ultimately, understand Long COVID in greater detail and hasten relief for the millions of people experiencing it. We examine the literature and investigate the current state, challenges, and opportunities of data sharing in Long COVID research.Significance: Although millions of people across the globe have been diagnosed with Long COVID, there still exist many research gaps in our understanding of the condition and its underlying causes. This work aims to elevate the discussion surrounding data sharing and data science in the research community and to engage data sharing as an enabler to fast-track the process of finding effective treatment for Long COVID.
Journal Article
An assessment of algorithms for deriving failure deterministic finite automata
Failure deterministic finite automata (FDFAs) represent regular languages more compactly than deterministic finite automata (DFAs). Four algorithms that convert arbitrary DFAs to language-equivalent FDFAs are empirically investigated. Three are concrete variants of a previously published abstract algorithm, the DFA-Homomorphic Algorithm (DHA). The fourth builds a maximal spanning tree from the DFA to derive what it calls a delayed input DFA. A first suite of test data consists of DFAs that recognise randomised sets of finite length keywords. Since the classical Aho-Corasick algorithm builds an optimal FDFA from such a set (and only from such a set), it provides benchmark FDFAs against which the performance of the general algorithms can be compared. A second suite of test data consists of random DFAs generated by a specially designed algorithm that also builds language-equivalent FDFAs, some of which may have non-divergent cycles. These random FDFAs provide (not necessarily tight) lower bounds for assessing the effectiveness of the four general FDFA generating algorithms.
Journal Article
From Tree Automata to String Automata Minimization
In this paper, we propose a reduction of the minimization problem for a bottom-up deterministic tree automaton (DFTA), making the latter a minimization of a string deterministic finite automaton (DFA). To achieve this purpose, we proceed first by the transformation of the tree automaton into a particular string automaton, followed by minimizing this string automaton. In addition, we show that for our transformation, the minimization of the resulting string automaton coincides with the minimization of the original tree automaton. Finally, we discuss the complexity of our proposal for different types of tree automata, namely: standard, acyclic, incremental, and incrementally constructed tree automata.
Journal Article
Weak factor automata: the failure of failure factor oracles?
In indexing of, and pattern matching on, DNA and text sequences, it is often important to represent all factors of a sequence. One efficient, compact representation is the factor oracle (FO). At the same time, any classical deterministic finite automata (DFA) can be transformed to a so-called failure one (FDFA), which may use failure transitions to replace multiple symbol transitions, potentially yielding a more compact representation. We combine the two ideas and directly construct a failure factor oracle (FFO) from a given sequence, in contrast to ex post facto transformation to an FDFA. The algorithm is suitable for both short and long sequences. We empirically compared the resulting FFOs and FOs on number of transitions for many DNA sequences of lengths 4 − 512, showing gains of up to 10% in total number of transitions, with failure transitions also taking up less space than symbol transitions. The resulting FFOs can be used for indexing, as well as in a variant of the FO-using backward oracle matching algorithm. We discuss and classify this pattern matching algorithm in terms of the keyword pattern matching taxonomies of Watson, Cleophas and Zwaan. We also empirically compared the use of FOs and FFOs in such backward reading pattern matching algorithms, using both DNA and natural language (English) data sets. The results indicate that the decrease in pattern matching performance of an algorithm using an FFO instead of an FO may outweigh the gain in representation space by using an FFO instead of an FO.
Journal Article
Crown in the Clouds: A Canadian Data Sovereignty Crisis
Cloud Service Providers (CSP) offer the opportunity for individuals, companies, and governments to rapidly leverage current capabilities dynamically and with great elasticity. At the time of writing, unlike the U.S., Canada does not have large sovereign CSPs with global presence. Although one may debate overall cost effectiveness and value of moving electronic data and computational workloads to the cloud, it is difficult to ignore the international drive for the adoption of cloud and cloud services. The Government of Canada's (GC) use of cloud capacities should not be driven by technical and economic desires or requirements. Like decision making in warfare, the complexities of international law and politics should be considered. This paper reflects upon the lessons learned from the U.S. \"Cloud First\" policy and discusses the challenges and risks of hosting sovereign Canadian electronic data and workloads in the cloud, concluding that is unclear if the GC will be able to claim data sovereignty even if its data is stored in Canada using a global CSP.
Conference Proceeding
Data sharing and data governance in sub-Saharan Africa: Perspectives from researchers and scientists engaged in data-intensive research
The data ecosystem is complex and involves multiple stakeholders. Researchers and scientists engaging in data-intensive research collect, analyse, store, manage and share large volumes of data. Consequently, capturing researchers' and scientists' views from multidisciplinary fields on data use, sharing and governance adds an impor tant African perspective to emerging debates. We conducted a descriptive cross-sectional survey and received 160 responses from researchers and scientists representing 43 sub-Saharan African countries. Whilst most respondents were satisfied with institutional data storage processes, 40% indicated that their organisations or institutions did not have a formally established process for storing data beyond the life cycle of the project. Willingness to share data was generally high, but increased when data privacy was ensured. Robust governance frameworks increased the willingness to share, as did the regulation of access to data on shared platforms. Incentivising data sharing remains controversial. Respondents were satisfied with exchanging their data for co-authorship on publications (89.4%) and collaboration on projects (77.6%). However, respondents were split almost equally in terms of sharing their data for commercial gain. Regarding the process of managing data, 40.6% indicated that their organisations do not provide training on best practices for data management. This could be related to a lack of resources, chronic institutional under-investment, and suboptimal research training and mentorship in sub-Saharan Africa. The sustainability of data sharing may require ethical incentive structures to fur ther encourage researchers and scientists. Tangible infrastructure to facilitate such sharing is a prerequisite. Capacity development in data governance for researchers and scientists is sorely needed.Significance: Data sharing is necessary to advance science, yet there are many constraints. In this study, we explored factors that promote a willingness to share, as well as constraining factors. Seeking potential solutions to improve data sharing is a scientific and ethical imperative. The standardisation of basic data sharing and data transfer agreements, and the development of a Data Access Committee will strengthen data governance and facilitate responsible data sharing in sub-Saharan Africa. Funders, institutions, researchers and scientists ought to jointly contribute to fair and equitable data use and sharing during and beyond the life cycle of research projects.
Journal Article
An efficient incremental DFA minimization algorithm
In this paper, we present a new Deterministic Finite Automata (DFA) minimization algorithm. The algorithm is incremental - it may be halted at any time, yielding a partially-minimized automaton. All of the other (known) minimization algorithms have intermediate results which are not useable for partial minimization. Since the first algorithm is easily understood but inefficient, we consider three practical and effective optimizations. The first two optimizations do not affect the asymptotic worst-case running time - though they perform well on a large class of automata. The third optimization yields an quadratic-time algorithm which is competitive with the previously known ones. [PUBLICATION ABSTRACT]
Journal Article